On-Device Artificial Intelligence for Image Analysis in Resource-Limited Settings: A Review

Medical imaging analysis in resource-limited settings face critical challenges, including inadequate computational infrastructure, unreliable connectivity, and a shortage of medical expertise.While cloud-based solutions have dominated medical artificial intelligence (AI), their reliance on stable internet and centralized data processing raises concerns about latency, privacy, and accessibility.On-device AI processes data locally on edge device and emerge as a transformative approach to address the given limitations.This review systematically explores the technical foundations, applications, and challenges of lightweight on-device AI for medical imaging in low-resource environments.Case studies across infectious disease detection using portable ultrasound, and retinal diagnostics demonstrate the feasibility of deploying compact models on devices such as Raspberry Pi-based platforms and smartphone.However, significant bottlenecks persist including hardware constraints, data scarcity, and ethical risks such as algorithmic bias.This study purpose the roadmap for future research to advocate for edge-cloud collaboration, bio-inspired ultra-low-power AI, and to bridge clinical and engineering domains.This review synthesizes cutting-edge advancements and provides practical insights for achieving equitable healthcare through decentralized and privacy-preserving AI solutions.